This is the first post in the series of posts related to Quality Assurance (QA) and Testing Practices and Data Science/Machine Learning Models, which I will release in the next few months. The goal of this and upcoming posts is to create a tool and framework that can help you design your testing/QA practices around data science/Machine Learning models.

QA Practices For Testing Machine Learning Models

Are you a test engineer and want to know how you can make a difference in the AI initiative being undertaken by your current company? Are you a QA manager and looking for or researching tools and frameworks that can help your team perform QA with Machine Learning models built by data scientists? Are you in one of the strategic roles in your company and looking for QA practices (to quality assure ML models built by data scientists) that you want to be adopted in your testing center of excellence (COE) to serve your clients in a better manner?

If the answers to the above questions are yes, then keep reading. I will be presenting concepts, tools, and frameworks that will help you achieve some of the objectives mentioned earlier.

I have seen in my experience that ML models are developed and tested by data scientists themselves. This is not a desired situation to be in. Ideally speaking, it should be a quality assurance team that should be performing QA by running tests as like traditional software to test the ML models from time-to-time. However, the challenge is that ML models are not like traditional software where the behavior of the software is pre-determined based on the different inputs. We will touch upon some of the challenges related to testing ML models in later articles.

What Can Be Tested With ML Models?

The following are some of the aspects of a Machine Learning model that needs to be tested/quality assured:

Quality of data

Quality of features

Quality of ML algorithms

Quality Assurance of Data Used for Training the Model

One of the most overlooked (or ignored) aspects of building a Machine Learning model is to check whether the data used for training and testing the model are sanitized or if they belong to an adversary data set. The adversary data sets are the ones that can be used to skew the results of the model by training the model using incorrect data. This is also termed as data poisoning attacks.

The role of the QA is to put test mechanisms in place to validate whether the data used for training is sanitized. In other words, the tests need to be performed to identify whether there are instances of data poisoning attacks intentionally or unintentionally.

In order to achieve the above, one of the techniques could be to have QA/Test engineers work with product management and product consultant teams for some of the following:

The parameters listed above would need to be tracked at regular intervals and verified with the help of PMs/consultants before every release. We will go into the details in later articles.

Quality Assurance of Features

Many a time, one or more features could cease to be important or become redundant/irrelevant, and, in turn, impact the prediction error rates. This is where a set of QA/testing practices should be in place to proactively evaluate features using feature engineering techniques such as feature selection, dimensionality reduction, etc. We will go into the details in later articles.

Quality Assurance of ML Algorithms

Evolving datasets as a result of business evolution or data poisoning attacks could result in increased prediction error rates. As the ML model gets retrained (manually or in an automated manner), the increased prediction error rates result in the re-evaluation of ML models, which could result in the discovery of new algorithms that could give improved accuracy over the previous ones.

One of the ways to go about testing ML algorithms with new data is the following:

Keep all the ML models based on different algorithms handy. Many times, I have seen that ML models are built using different algorithms and get discarded once and for all after the most accurate model gets selected.

Retrain all of the models and evaluate the performance

Track the performance of all the models with new data set at regular intervals.

Raise the defect if another model starts giving greater accuracy or performing better than the existing model.

We will go into further details in later articles.

References

Summary

In this post, you learned about the need for QA practices for Data Science/ML models and also the different aspects of testing the ML models. Please feel free to suggest or share your thoughts in the comments section.

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